dimension op• Data kinds: conn_graph × labels → conn_graph
• Call: import fullseye as fs; fs.ledger.graph_block_shuffle(W: 'Any', labels: 'Any', seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import conngraph; conngraph.graph_block_shuffle(W: 'Any', labels: 'Any', seed: 'int' = 0) -> 'np.ndarray'; from the registry, opsconngraph.get("graph_block_shuffle"))
A control conn_graph in which the senders are shuffled within each block of layers (labels): it keeps the multiset of weights each receiver gets and the total amount of wiring between layers, and destroys only the "from whom".
Whereas graph_degree_preserving_shuffle preserves the overall degree sequence, this one preserves the layer structure (the blocks brain → neck → ventral nerve cord → muscle are not moved; rows are permuted within each block). Used as a control that keeps the effects of layer size and convergence (fan-in) and removes only "the specificity of the wiring". Reproducible with the same seed.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_connectome_motor_bottleneck — py -3.11 examples/poc_connectome_motor_bottleneck.py
conn_graph as input)graph_degree_preserving_shuffle · graph_binarize · graph_degree_table · graph_clustering_coefficient · graph_betweenness · graph_laplacian_spectrum · graph_spectral_radius · graph_components
dimension)graph_layer_propagate · states_participation_ratio · states_layer_dimension
*Provenance: conngraph.py — CONNGRAPH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.